Philippe Gluckman, D. Minter, Kendal Chronkhite, Cassidy J. Curtis, Milana Huang, Rob Vogt, Scott Singer
{"title":"Session details: \"Madagascar:\" bringing a new visual style to the screen","authors":"Philippe Gluckman, D. Minter, Kendal Chronkhite, Cassidy J. Curtis, Milana Huang, Rob Vogt, Scott Singer","doi":"10.1145/3245699","DOIUrl":"https://doi.org/10.1145/3245699","url":null,"abstract":"","PeriodicalId":192758,"journal":{"name":"ACM SIGGRAPH 2005 Courses","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124719941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Rusinkiewicz, Forrester Cole, D. DeCarlo, Adam Finkelstein
{"title":"Line drawings from 3D models","authors":"S. Rusinkiewicz, Forrester Cole, D. DeCarlo, Adam Finkelstein","doi":"10.1145/1198555.1198577","DOIUrl":"https://doi.org/10.1145/1198555.1198577","url":null,"abstract":"","PeriodicalId":192758,"journal":{"name":"ACM SIGGRAPH 2005 Courses","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124532703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computational photography combines plentiful computing, digital sensors, modern optics, actuators, probes and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography. The computational techniques encompass methods from modification of imaging parameters during capture to sophisticated reconstructions from indirect measurements. We provide a practical guide to topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. Many ideas in computational photography are still relatively new to digital artists and programmers and there is no upto-date reference text. A larger problem is that a multi-disciplinary field that combines ideas from computational methods and modern digital photography involves a steep learning curve. For example, photographers are not always familiar with advanced algorithms now emerging to capture high dynamic range images, but image processing researchers face difficulty in understanding the capture and noise issues in digital cameras. These topics, however, can be easily learned without extensive background. The goal of this STAR is to present both aspects in a compact form. The new capture methods include sophisticated sensors, electromechanical actuators and on-board processing. Examples include adaptation to sensed scene depth and illumination, taking multiple pictures by varying camera parameters or actively modifying the flash illumination parameters. A class of modern reconstruction methods is also emerging. The methods can achieve a ‘photomontage’ by optimally fusing information from multiple images, improve signal to noise ratio and extract scene features such as depth edges. The STAR briefly reviews fundamental topics in digital imaging and then provides a practical guide to underlying techniques beyond image processing such as gradient domain operations, graph cuts, bilateral filters and optimizations. The participants learn about topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. We hope to provide enough fundamentals to satisfy the technical specialist without intimidating the curious graphics researcher interested in recent advances in photography. The intended audience is photographers, digital artists, image processing programmers and vision researchers using or building applications for digital cameras or images. They will learn about camera fundamentals and powerful computational tools, along with many real world examples.
{"title":"Computational photography","authors":"Ramesh Raskar, J. Tumblin","doi":"10.1145/1198555.1198561","DOIUrl":"https://doi.org/10.1145/1198555.1198561","url":null,"abstract":"Computational photography combines plentiful computing, digital sensors, modern optics, actuators, probes and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography. The computational techniques encompass methods from modification of imaging parameters during capture to sophisticated reconstructions from indirect measurements. We provide a practical guide to topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. Many ideas in computational photography are still relatively new to digital artists and programmers and there is no upto-date reference text. A larger problem is that a multi-disciplinary field that combines ideas from computational methods and modern digital photography involves a steep learning curve. For example, photographers are not always familiar with advanced algorithms now emerging to capture high dynamic range images, but image processing researchers face difficulty in understanding the capture and noise issues in digital cameras. These topics, however, can be easily learned without extensive background. The goal of this STAR is to present both aspects in a compact form. The new capture methods include sophisticated sensors, electromechanical actuators and on-board processing. Examples include adaptation to sensed scene depth and illumination, taking multiple pictures by varying camera parameters or actively modifying the flash illumination parameters. A class of modern reconstruction methods is also emerging. The methods can achieve a ‘photomontage’ by optimally fusing information from multiple images, improve signal to noise ratio and extract scene features such as depth edges. The STAR briefly reviews fundamental topics in digital imaging and then provides a practical guide to underlying techniques beyond image processing such as gradient domain operations, graph cuts, bilateral filters and optimizations. The participants learn about topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples. We hope to provide enough fundamentals to satisfy the technical specialist without intimidating the curious graphics researcher interested in recent advances in photography. The intended audience is photographers, digital artists, image processing programmers and vision researchers using or building applications for digital cameras or images. They will learn about camera fundamentals and powerful computational tools, along with many real world examples.","PeriodicalId":192758,"journal":{"name":"ACM SIGGRAPH 2005 Courses","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130195902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Session details: Developing mobile 3D applications with OpenGL ES and M3G","authors":"","doi":"10.1145/3245729","DOIUrl":"https://doi.org/10.1145/3245729","url":null,"abstract":"","PeriodicalId":192758,"journal":{"name":"ACM SIGGRAPH 2005 Courses","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122325332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brian K. Guenter, Cindy Grimm, Daniel Wood, Henrique S. Malvar, Frédéric H. Pighin
We have created a system for capturing both the three-dimensional geometry and color and shading information for human facial expressions. We use this data to reconstruct photorealistic, 3D animations of the captured expressions. The system uses a large set of sampling points on the face to accurately track the three dimensional deformations of the face. Simultaneously with the tracking of the geometric data, we capture multiple high resolution, registered video images of the face. These images are used to create a texture map sequence for a three dimensional polygonal face model which can then be rendered on standard 3D graphics hardware. The resulting facial animation is surprisingly life-like and looks very much like the original live performance. Separating the capture of the geometry from the texture images eliminates much of the variance in the image data due to motion, which increases compression ratios. Although the primary emphasis of our work is not compression we have investigated the use of a novel method to compress the geometric data based on principal components analysis. The texture sequence is compressed using an MPEG4 video codec. Animations reconstructed from 512x512 pixel textures look good at data rates as low as 240 Kbits per second.
{"title":"Making faces","authors":"Brian K. Guenter, Cindy Grimm, Daniel Wood, Henrique S. Malvar, Frédéric H. Pighin","doi":"10.1145/1198555.1198590","DOIUrl":"https://doi.org/10.1145/1198555.1198590","url":null,"abstract":"We have created a system for capturing both the three-dimensional geometry and color and shading information for human facial expressions. We use this data to reconstruct photorealistic, 3D animations of the captured expressions. The system uses a large set of sampling points on the face to accurately track the three dimensional deformations of the face. Simultaneously with the tracking of the geometric data, we capture multiple high resolution, registered video images of the face. These images are used to create a texture map sequence for a three dimensional polygonal face model which can then be rendered on standard 3D graphics hardware. The resulting facial animation is surprisingly life-like and looks very much like the original live performance. Separating the capture of the geometry from the texture images eliminates much of the variance in the image data due to motion, which increases compression ratios. Although the primary emphasis of our work is not compression we have investigated the use of a novel method to compress the geometric data based on principal components analysis. The texture sequence is compressed using an MPEG4 video codec. Animations reconstructed from 512x512 pixel textures look good at data rates as low as 240 Kbits per second.","PeriodicalId":192758,"journal":{"name":"ACM SIGGRAPH 2005 Courses","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：